Selecting optimal hypervisor platforms that satisfy application workload requirements

ABSTRACT

A method, system and computer program product for selecting hypervisor platforms that are best suited to process application workloads. Attribute requirements for an application workload, such as high CPU capacity, high power and low cost, are received. A ranking algorithm is then applied to a list of pools of compute nodes to identify an ordered list of pools of compute nodes that are best suited for satisfying the attribute requirements of the application workload by comparing hypervisor characteristics of the pools of compute nodes with the attribute requirements of the application workload. Each pool of compute nodes runs on a particular hypervisor platform which has a unique combination of characteristics that correspond to a combination of a set of attribute requirements (e.g., medium CPU/memory/disk capacity; high CPU and memory performance). In this manner, the hypervisor platforms that are best suited for satisfying the application workload requirements are identified.

TECHNICAL FIELD

The present invention relates generally to cloud computing, and moreparticularly to selecting optimal hypervisor platforms that satisfyapplication workload requirements at workload provisioning time.

BACKGROUND

In a cloud computing environment, computing is delivered as a servicerather than a product, whereby shared resources, software andinformation are provided to computers and other devices as a meteredservice over a network, such as the Internet. In such an environment,computation, software, data access and storage services are provided tousers that do not require knowledge of the physical location andconfiguration of the system that delivers the services.

In a virtualized computer environment, such as may be implemented in aphysical cloud computing node of the cloud computing environment, thevirtualized computer environment includes a virtual operating system.The virtual operating system includes a common base portion and separateuser portions that all run on a physical computer. The physical computeris referred to as a host. The common base portion may be referred to asa hypervisor and each user portion may be called a guest. Each guest isa logical partition of the physical resources of the computer. A guestoperating system runs on each guest, and the guest appears to the guestoperating system as a real computer. Each guest operating system mayhost one or more virtual machines.

Currently, functions of the cloud computing environment are performed atleast in part by hardware components, such as blade servers, which mayrun different hypervisor platforms (e.g., PowerVM®, VMware® ESX, OpenKVM). Each of these hypervisor platforms may exhibit strengths orweaknesses in comparison to the other hypervisor platforms. For example,one hypervisor platform may provide an effective input/output rate whilehaving lower memory density in comparison to other hypervisor platforms.In another example, one hypervisor platform may provide the lowestoverhead in Central Processing Unit (CPU) virtualization while havinglow disk performance in comparison to other hypervisor platforms.Similarly, application workloads (referring to the amount of processingthat a hardware component has been given to do at a given time) that areprovisioned on the cloud computing environment have different needs orrequirements. For example, one application workload may be dependent onCPU computing efficiency while another application workload may bedependent on network latency.

Unfortunately, there is not currently a means for selecting thehypervisor platforms that are best suited for satisfying the applicationworkload requirements. As a result, the application workloads may not beeffectively processed.

BRIEF SUMMARY

In one embodiment of the present invention, a method for selectinghypervisor platforms that are best suited to process applicationworkloads comprises receiving attribute requirements for an applicationworkload. The method further comprises applying, by a processor, aranking algorithm to a list of pools of compute nodes to identify anordered list of pools of compute nodes that are best suited forsatisfying the attribute requirements of the application workload bycomparing hypervisor characteristics of the pools of compute nodes withthe attribute requirements of the application workload. Each of thepools of compute nodes comprises a set of compute nodes that run on aparticular hypervisor platform, where the particular hypervisor platformhas a unique combination of characteristics that correspond to acombination of a set of attribute requirements. Additionally, the methodcomprises displaying the ordered list of pools of compute nodes that arebest suited for satisfying the attribute requirements of the applicationworkload.

Other forms of the embodiment of the method described above are in asystem and in a computer program product.

The foregoing has outlined rather generally the features and technicaladvantages of one or more embodiments of the present invention in orderthat the detailed description of the present invention that follows maybe better understood. Additional features and advantages of the presentinvention will be described hereinafter which may form the subject ofthe claims of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

A better understanding of the present invention can be obtained when thefollowing detailed description is considered in conjunction with thefollowing drawings, in which:

FIG. 1 illustrates a network system configured in accordance with anembodiment of the present invention;

FIG. 2 illustrates a cloud computing environment in accordance with anembodiment of the present invention.

FIG. 3 illustrates a schematic of a rack of compute nodes of the cloudcomputing node that is managed by an administrative server in accordancewith an embodiment of the present invention;

FIG. 4 illustrates a virtualization environment for a compute node inaccordance with an embodiment of the present invention;

FIG. 5 illustrates a hardware configuration of an administrative serverconfigured in accordance with an embodiment of the present invention;

FIG. 6 is a flowchart of a method for selecting the hypervisor platformsthat are best suited for satisfying the application workloadrequirements in accordance with an embodiment of the present invention;and

FIG. 7 is a dependency graph for illustrating the feasible combinationsof workload attribute requirements for different levels of attributepriority that are supported by the hypervisor platforms of the pools ofcompute nodes in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention comprises a method, system and computer programproduct for selecting hypervisor platforms that are best suited toprocess application workloads. In one embodiment of the presentinvention, attribute requirements for an application workload, such ashigh CPU capacity, high power and low cost, are received. A rankingalgorithm is then applied to a list of pools of compute nodes toidentify an ordered list of pools of compute nodes that are best suitedfor satisfying the attribute requirements of the application workload bycomparing hypervisor characteristics of the pools of compute nodes withthe attribute requirements of the application workload. Each pool ofcompute nodes runs on a particular hypervisor platform which has aunique combination of characteristics that correspond to a combinationof a set of attribute requirements (e.g., medium CPU/memory/diskcapacity; high CPU and memory performance; low disk performance; mediumcost; high power; and a low guarantee on SLA policies). The ordered listof pools of compute nodes that are best suited for satisfying theattribute requirements of the application workload are then displayed.In this manner, the hypervisor platforms that are best suited forsatisfying the application workload requirements are identified.

In the following description, numerous specific details are set forth toprovide a thorough understanding of the present invention. However, itwill be apparent to those skilled in the art that the present inventionmay be practiced without such specific details. In other instances,well-known circuits have been shown in block diagram form in order notto obscure the present invention in unnecessary detail. For the mostpart, details considering timing considerations and the like have beenomitted inasmuch as such details are not necessary to obtain a completeunderstanding of the present invention and are within the skills ofpersons of ordinary skill in the relevant art.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,the embodiments of the present invention are capable of beingimplemented in conjunction with any type of clustered computingenvironment now known or later developed.

In any event, the following definitions have been derived from the “TheNIST Definition of Cloud Computing” by Peter Mell and Timothy Grance,dated September 2011, which is cited on an Information DisclosureStatement filed herewith, and a copy of which is provided to the U.S.Patent and Trademark Office.

Cloud computing is a model for enabling ubiquitous, convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications, and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. This cloud model is composed offive essential characteristics, three service models, and fourdeployment models.

Characteristics are as follows:

On-Demand Self-Service: A consumer can unilaterally provision computingcapabilities, such as server time and network storage, as needed,automatically without requiring human interaction with each service'sprovider.

Broad Network Access: Capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, tablets, laptopsand workstations).

Resource Pooling: The provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according toconsumer demand. There is a sense of location independence in that theconsumer generally has no control or knowledge over the exact locationof the provided resources but may be able to specify location at ahigher level of abstraction (e.g., country, state or data center).Examples of resources include storage, processing, memory and networkbandwidth.

Rapid Elasticity: Capabilities can be elastically provisioned andreleased, in some cases automatically, to scale rapidly outward andinward commensurate with demand. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured Service: Cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth and active user accounts). Resource usage can bemonitored, controlled and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): The capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices througheither a thin client interface, such as a web browser (e.g., web-basede-mail) or a program interface. The consumer does not manage or controlthe underlying cloud infrastructure including network, servers,operating systems, storage, or even individual application capabilities,with the possible exception of limited user-specific applicationconfiguration settings.

Platform as a Service (PaaS): The capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages, libraries, servicesand tools supported by the provider. The consumer does not manage orcontrol the underlying cloud infrastructure including networks, servers,operating systems or storage, but has control over the deployedapplications and possibly configuration settings for theapplication-hosting environment.

Infrastructure as a Service (IaaS): The capability provided to theconsumer is to provision processing, storage, networks and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage anddeployed applications; and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private Cloud: The cloud infrastructure is provisioned for exclusive useby a single organization comprising multiple consumers (e.g., businessunits). It may be owned, managed and operated by the organization, athird party or some combination of them, and it may exist on or offpremises.

Community Cloud: The cloud infrastructure is provisioned for exclusiveuse by a specific community of consumers from organizations that haveshared concerns (e.g., mission, security requirements, policy andcompliance considerations). It may be owned, managed and operated by oneor more of the organizations in the community, a third party, or somecombination of them, and it may exist on or off premises.

Public Cloud: The cloud infrastructure is provisioned for open use bythe general public. It may be owned, managed and operated by a business,academic or government organization, or some combination of them. Itexists on the premises of the cloud provider.

Hybrid Cloud: The cloud infrastructure is a composition of two or moredistinct cloud infrastructures (private, community or public) thatremain unique entities, but are bound together by standardized orproprietary technology that enables data and application portability(e.g., cloud bursting for load balancing between clouds).

Referring now to the Figures in detail, FIG. 1 illustrates a networksystem 100 configured in accordance with an embodiment of the presentinvention. Network system 100 includes a client device 101 connected toa cloud computing environment 102 via a network 103. Client device 101may be any type of computing device (e.g., portable computing unit,Personal Digital Assistant (PDA), smartphone, laptop computer, mobilephone, navigation device, game console, desktop computer system,workstation, Internet appliance and the like) configured with thecapability of connecting to cloud computing environment 102 via network103.

Network 103 may be, for example, a local area network, a wide areanetwork, a wireless wide area network, a circuit-switched telephonenetwork, a Global System for Mobile Communications (GSM) network,Wireless Application Protocol (WAP) network, a WiFi network, an IEEE802.11 standards network, various combinations thereof, etc. Othernetworks, whose descriptions are omitted here for brevity, may also beused in conjunction with system 100 of FIG. 1 without departing from thescope of the present invention.

Cloud computing environment 102 is used to deliver computing as aservice to client device 101 implementing the model discussed above. Anembodiment of cloud computing environment 102 is discussed below inconnection with FIG. 2.

FIG. 2 illustrates cloud computing environment 102 in accordance with anembodiment of the present invention. As shown, cloud computingenvironment 102 includes one or more cloud computing nodes 201 (alsoreferred to as “clusters”) with which local computing devices used bycloud consumers, such as, for example, Personal Digital Assistant (PDA)or cellular telephone 202, desktop computer 203, laptop computer 204,and/or automobile computer system 205 may communicate. Nodes 201 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 102 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. Cloud computing nodes 201 may include one or more racks ofcompute nodes (e.g., servers) that are managed by a server (referred toherein as the “administrative server”) in cloud computing environment102 as discussed below in greater detail in connection with FIG. 3.

It is understood that the types of computing devices 202, 203, 204, 205shown in FIG. 2, which may represent client device 101 of FIG. 1, areintended to be illustrative and that cloud computing nodes 201 and cloudcomputing environment 102 can communicate with any type of computerizeddevice over any type of network and/or network addressable connection(e.g., using a web browser). Program code located on one of nodes 201may be stored on a computer recordable storage medium in one of nodes201 and downloaded to computing devices 202, 203, 204, 205 over anetwork for use in these computing devices. For example, a servercomputer in computing nodes 201 may store program code on a computerreadable storage medium on the server computer. The server computer maydownload the program code to computing device 202, 203, 204, 205 for useon the computing device.

Referring now to FIG. 3, FIG. 3 illustrates a schematic of a rack ofcompute nodes (e.g., servers) of a cloud computing node 201 that aremanaged by an administrative server in accordance with an embodiment ofthe present invention.

As shown in FIG. 3, cloud computing node 201 may include a rack 301 ofhardware components or “compute nodes,” such as servers or otherelectronic devices. For example, rack 301 houses compute nodes302A-302E. Compute nodes 302A-302E may collectively or individually bereferred to as compute nodes 302 or compute node 302, respectively. Anillustrative virtualization environment for compute node 302 isdiscussed further below in connection with FIG. 4. FIG. 3 is not to belimited in scope to the number of racks 301 or compute nodes 302depicted. For example, cloud computing node 201 may be comprised of anynumber of racks 301 which may house any number of compute nodes 302.Furthermore, while FIG. 3 illustrates rack 301 housing compute nodes302, rack 301 may house any type of computing component that is used bycloud computing node 201. Furthermore, while the following discussescompute node 302 being confined in a designated rack 301, it is notedfor clarity that compute nodes 302 may be distributed across cloudcomputing environment 102 (FIGS. 1 and 2).

As discussed in further detail below, a pool or a set of compute nodes302 (e.g., compute nodes 302A-302B) runs on a particular hypervisorplatform (e.g., PowerVM®, VMware® ESX, Open KVM) with a uniquecombination of characteristics that correspond to a combination ofrating levels for possible attributes of an application workload. Forexample, attributes of an application workload may include, but notlimited to, Central Processing Unit (CPU) capacity, memory capacity,disk capacity, network capacity, CPU performance, memory performance,disk performance, network performance, power consumption, cost ofservice, reliability requirements and Service Level Agreement (SLA)policies. The particular hypervisor platform may be rated based on howwell they perform or satisfy these attributes. For example, a hypervisorplatform may be rated as providing “high” CPU and memory performance butrated as providing “low” disk performance. In another example, ahypervisor platform may be rated as providing “medium” CPU/memory/diskcapacity while providing “medium” cost, “high” power and “low” guaranteeon SLA policies. The hypervisor platform in each pool of compute nodes302 may possess unique characteristics. That is, the hypervisor platformin each pool of compute nodes 302 may include a unique combination ofrating levels for possible attributes of an application workload.

As discussed above, cloud computing environment 102 may include anynumber of cloud computing nodes 201, where each cloud computing node mayinclude any number of racks 301 of compute nodes 302. Cloud computingenvironment 102 may include any number of pools of compute nodes 302(e.g., compute nodes 302A-302B may represent one pool of compute nodes;whereas, compute nodes 302C-302E may represent another pool of computenodes), where each pool or set of compute nodes 302 runs on a particularhypervisor platform (e.g., PowerVM®, VMware® ESX, Open KVM) that mayinclude a unique combination of rating levels for possible attributes ofan application workload.

As further shown in FIG. 3, rack 301 is coupled to an administrativeserver 303 configured to provide data center-level functions.Administrative server 303 supports a module, referred to herein as themanagement software 304, that can be used to manage all the computenodes 302 of cloud computing node 201, monitor system utilization,intelligently deploy images of data and optimize the operations of cloudcomputing environment 102. Furthermore, management software 304 may beused to select the hypervisor platforms in the pools of compute nodes302 that are best suited for satisfying the application workloadrequirements as discussed further below. A description of the hardwareconfiguration of administrative server 303 is provided further below inconnection with FIG. 5.

Referring now to FIG. 4, FIG. 4 illustrates a virtualization environmentfor compute node 302 (FIG. 3) in accordance with an embodiment of thepresent invention. Compute node 302 includes a virtual operating system401. Operating system 401 executes on a real or physical computer 402.Real computer 402 includes one or more processors 403, a memory 404(also referred to herein as the host physical memory), one or more diskdrives 405 and the like. Other components of real computer 402 are notdiscussed herein for the sake of brevity.

Virtual operating system 401 further includes user portions 406A-406B(identified as “Guest 1” and “Guest 2,” respectively, in FIG. 4),referred to herein as “guests.” Each guest 406A, 406B is capable offunctioning as a separate system. That is, each guest 406A-406B can beindependently reset, host a guest operating system 407A-407B,respectively, (identified as “Guest 1 O/S” and “Guest 2 O/S,”respectively, in FIG. 4) and operate with different programs. Anoperating system or application program running in guest 406A, 406Bappears to have access to a full and complete system, but in reality,only a portion of it is available. Guests 406A-406B may collectively orindividually be referred to as guests 406 or guest 406, respectively.Guest operating systems 407A-407B may collectively or individually bereferred to as guest operating systems 407 or guest operating system407, respectively.

Each guest operating system 407A, 407B may host one or more virtualmachine applications 408A-408C (identified as “VM 1,” “VM 2” and “VM 3,”respectively, in FIG. 4), such as Java™ virtual machines. For example,guest operating system 407A hosts virtual machine applications408A-408B. Guest operating system 407B hosts virtual machine application408C. Virtual machines 408A-408C may collectively or individually bereferred to as virtual machines 408 or virtual machine 408,respectively.

Virtual operating system 401 further includes a common base portion 409,referred to herein as a hypervisor. Hypervisor 409 may be implemented inmicrocode running on processor 403 or it may be implemented in softwareas part of virtual operating system 401. Hypervisor 409 is configured tomanage and enable guests 406 to run on a single host. As discussedabove, a pool of compute nodes 302 runs on a particular hypervisorplatform 409 (e.g., PowerVM®, VMware® ESX, Open KVM) that may include aunique combination of rating levels for possible attributes of anapplication workload. For example, if compute nodes 302A-302B representa pool of compute nodes 302, then compute nodes 302A-302B each run onthe same hypervisor platform 409 (e.g., PowerVM®).

As discussed above, virtual operating system 401 and its componentsexecute on physical or real computer 402. These software components maybe loaded into memory 404 for execution by processor 403.

The virtualization environment for compute node 302 is not to be limitedin scope to the elements depicted in FIG. 4. The virtualizationenvironment for compute node 302 may include other components that werenot discussed herein for the sake of brevity.

Referring now to FIG. 5, FIG. 5 illustrates a hardware configuration ofadministrative server 303 (FIG. 3) which is representative of a hardwareenvironment for practicing the present invention. Administrative server303 has a processor 501 coupled to various other components by systembus 502. An operating system 503 runs on processor 501 and providescontrol and coordinates the functions of the various components of FIG.5. An application 504 in accordance with the principles of the presentinvention runs in conjunction with operating system 503 and providescalls to operating system 503 where the calls implement the variousfunctions or services to be performed by application 504. Application504 may include, for example, a program (e.g., management software 304of FIG. 3) for selecting the hypervisor platforms 409 in the pools ofcompute nodes 302 that are best suited for satisfying the applicationworkload requirements as discussed further below in association withFIGS. 6 and 7.

Referring again to FIG. 5, read-only memory (“ROM”) 505 is coupled tosystem bus 502 and includes a basic input/output system (“BIOS”) thatcontrols certain basic functions of administrative server 303. Randomaccess memory (“RAM”) 506 and disk adapter 507 are also coupled tosystem bus 502. It should be noted that software components includingoperating system 503 and application 504 may be loaded into RAM 506,which may be administrative server's 303 main memory for execution. Diskadapter 507 may be an integrated drive electronics (“IDE”) adapter thatcommunicates with a disk unit 508, e.g., disk drive. It is noted thatthe program for selecting the hypervisor platforms 409 in the pools ofcompute nodes 302 that are best suited for satisfying the applicationworkload requirements, as discussed further below in association withFIGS. 6 and 7, may reside in disk unit 508 or in application 504.

Administrative server 303 may further include a communications adapter509 coupled to bus 502. Communications adapter 509 interconnects bus 502with an outside network (e.g., network 103 of FIG. 1).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As stated in the Background section, in a virtualized computerenvironment, such as may be implemented in a physical cloud computingnode of the cloud computing environment, the virtualized computerenvironment includes a virtual operating system. The virtual operatingsystem includes a common base portion and separate user portions thatall run on a physical computer. The physical computer is referred to asa host. The common base portion may be referred to as a hypervisor andeach user portion may be called a guest. Each guest is a logicalpartition of the physical resources of the computer. A guest operatingsystem runs on each guest, and the guest appears to the guest operatingsystem as a real computer. Each guest operating system may host one ormore virtual machines. Currently, functions of the cloud computingenvironment are performed at least in part by hardware components, suchas blade servers, which may run different hypervisor platforms (e.g.,PowerVM®, VMware® ESX, Open KVM). Each of these hypervisor platforms mayexhibit strengths or weakness in comparison to the other hypervisorplatforms. For example, one hypervisor platform may provide an effectiveinput/output rate while having lower memory density in comparison toother hypervisor platforms. In another example, one hypervisor platformmay provide the lowest overhead in Central Processing Unit (CPU)virtualization while having low disk performance in comparison to otherhypervisor platforms. Similarly, application workloads (referring to theamount of processing that a hardware component has been given to do at agiven time) that are provisioned on the cloud computing environment havedifferent needs or requirements. For example, one application workloadmay be dependent on CPU computing efficiency while another applicationworkload may be dependent on network latency. Unfortunately, there isnot currently a means for selecting the hypervisor platforms that arebest suited for satisfying the application workload requirements. As aresult, the application workloads may not be effectively processed.

The principles of the present invention provide a means for selectingthe hypervisor platforms that are best suited for satisfying theapplication workload requirements as discussed below in association withFIGS. 6 and 7. FIG. 6 is a flowchart of a method for selecting thehypervisor platforms that are best suited for satisfying the applicationworkload requirements. FIG. 7 is a dependency graph for illustrating thefeasible combinations of workload attribute requirements for differentlevels of attribute priority that are supported by the hypervisorplatforms of the pools of compute nodes.

As discussed above, FIG. 6 is a flowchart of a method 600 for selectingthe hypervisor platforms that are best suited for satisfying theapplication workload requirements in accordance with an embodiment ofthe present invention.

Referring to FIG. 6, in conjunction with FIGS. 1-5, in step 601,administrative server 303 receives the attribute requirements for anapplication workload. Examples of the attributes of the applicationworkload include, but not limited to, Central Processing Unit (CPU)capacity, memory capacity, disk capacity, network capacity, CPUperformance, memory performance, disk performance, network performance,power consumption, cost of service, reliability requirements and servicelevel agreement policies. “Attribute requirements,” as used herein,refer to the required level of performance concerning the attribute. Forexample, an application workload may require high CPU capacity, high CPUperformance and low power consumption. Attribute requirements may besaid to include a “rating level” which indicates the required level ofperformance. For instance, the attribute requirement of high CPUcapacity may be said to include the rating level of “high.”

In one embodiment, administrative server 303 may receive the attributerequirements for an application workload at the deployment submissiontime by the user (e.g., user of client device 101) of the workload.Furthermore, the attribute requirements for the application workload maybe determined at runtime and stored as metadata with a deploymentartifact. Administrative server 303 would then receive such attributerequirements (stored as metadata) at the deployment submission time viathe deployment artifact. For example, the attribute requirements of theapplication workload may be determined based on observing the workloadat runtime. For instance, the application workload may be determined tobe highly input/output intensive based on the number of input/outputoperations requested to be performed. Such attribute requirements may bestored as metadata. In another example, the attribute requirements ofthe application workload may be determined based on previous deploymentsof the application workload which are stored as metadata with adeployment artifact.

Upon receiving the attribute requirements of the application workload,method 600 utilizes one of two approaches to identify the list of poolsof compute nodes 302 that are best suited for satisfying the attributerequirements of the application workload as discussed below. Oneapproach is discussed below in connection with steps 602-606; whereas,the other approach is discussed below in connection with steps 607-614.

In step 602, administrative server 303 constructs a tuple vector for theapplication workload using the attribute requirements of the applicationworkload. For example, administrative server 303 may construct the tuplevector of <high CPU capacity, low memory capacity, low disk capacity,low cost and low power> based on the application workload's attributerequirements of high CPU capacity, low memory capacity, low diskcapacity, low cost and low power.

In step 603, administrative server 303 constructs vector, referred toherein as the “hypervisor pool vector,” for each hypervisor platform 409of the pools of compute nodes 302 using the unique combination ofcharacteristics of hypervisor platform 409 that correspond to acombination of attribute requirements. For example, hypervisor platform409 for a pool of compute nodes 302 may exhibit the characteristics ofhaving medium CPU/memory/disk capacity; high CPU and memory performance;low disk performance; medium cost; high power; and a low guarantee onSLA policies. These characteristics may be used to construct ahypervisor pool vector, such as <medium CPU capacity, medium memorycapacity, medium disk capacity, high CPU performance, high memoryperformance, low disk performance, medium cost, high power and lowguarantee on SLA policies>.

In step 604, administrative server 303 compares the tuple vector of theapplication workload with the hypervisor pool vector for each particularhypervisor platform 409 of the pools of compute nodes 302. That is,administrative server 303 compares the tuple vector of the applicationworkload with the hypervisor pool vector for each pool of compute nodes302 running a particular type of hypervisor platform 409.

In step 605, administrative server 303 applies a ranking algorithm to alist of pools of compute nodes 302 (e.g., compute nodes 302A-302B mayrepresent one pool of compute nodes; whereas, compute nodes 302C-302Emay represent another pool of compute nodes) using the comparison ofstep 604 to identify an ordered list of pools of compute nodes 302 thatare best suited for satisfying the attribute requirements of theapplication workload. For example, the ranking algorithm may be based onminimizing the number of disagreements. Hence, the pools of computenodes 302 with a hypervisor platform 409 that have a hypervisor poolvector with the fewest number of disagreements with the tuple vector ofthe application workload are identified. For instance, the hypervisorpool vector of <high CPU capacity, low memory capacity, low diskcapacity, high cost and low power> only has a single disagreement withthe tuple vector of <high CPU capacity, low memory capacity, low diskcapacity, low cost and low power> and would be identified (i.e., thepool of compute nodes 302 with such a hypervisor platform 409 containingsuch a hypervisor pool vector) as having a higher ranking than otherhypervisor platforms 409 (i.e., the pool of compute nodes 302 with otherhypervisor platforms 409) with a hypervisor pool vector that has morethan a single disagreement with the tuple vector of the applicationworkload.

In another example, the ranking algorithm may be based on maximizing thenumber of agreements. Hence, the pools of compute nodes 302 with ahypervisor platform 409 that have a hypervisor pool vector with thegreatest number of agreements with the tuple vector of the applicationworkload are identified. For instance, the hypervisor pool vector of<high CPU capacity, low memory capacity, medium disk capacity, high CPUperformance, high memory performance, low disk performance, low cost,high power and low guarantee on SLA policies> matches three attributerequirements with the tuple vector of <high CPU capacity, low memorycapacity, low disk capacity, low cost and low power> and would beidentified (i.e., the pool of compute nodes 302 with such a hypervisorplatform 409 containing such a hypervisor pool vector) as having ahigher ranking than other hypervisor platforms 409 (i.e., the pool ofcompute nodes 302 with other hypervisor platforms 409) with a hypervisorpool vector that matches with less than three attribute requirementswith the tuple vector of the application workload. In this manner, thehypervisor platforms 409 that are best suited for satisfying theapplication workload requirements are identified.

In step 606, administrative server 303 displays, such as via a displayof a user computing device (e.g., client device 101), the ordered listof the pools of compute nodes 302 that are best suited for satisfyingthe attribute requirements of the application workload.

An alternative approach to steps 602-606 is discussed below inconnection with steps 607-614.

In step 607, administrative server 303 generates a dependency graphillustrating the feasible combinations of workload attributerequirements for different levels of attribute priority that aresupported by hypervisor platforms 409 of the pools of compute nodes 302as illustrated in FIG. 7.

FIG. 7 is a dependency graph 700 for illustrating the feasiblecombinations of workload attribute requirements for different levels ofattribute priority that are supported by hypervisor platforms 409 (FIG.4) of the pools of compute nodes 302 (FIG. 3) in accordance with anembodiment of the present invention.

Referring to FIG. 7, dependency graph 700 illustrates that the attributeof cost has the highest priority, followed by the attribute of memorycapacity which has the second highest priority followed by the attributeof CPU performance which has the third highest priority. For the ratingof “low” for the attribute of cost, the feasible combination includes arating of “low” for the attribute of memory capacity and a rating ofeither “low” or “medium” for the attribute of CPU performance. Thesefeasible combinations are identified by the arrows in FIG. 7. Theinfeasible combinations are shaded darker than the feasible combinationsin FIG. 7. For example, the attribute requirement of low cost is notcompatible with the attribute requirements of high CPU performance andhigh memory capacity. The terms “L,” “M,” and “H” in FIG. 7 correspondto the rating of “low,” “medium” and “high,” respectively.

Returning to FIG. 6, in conjunction with FIGS. 1-5 and 7, in step 608,administrative server 303 receives a level of priority from the user(e.g., user of client device 101) for each attribute requirement of theapplication workload to form a list of workload attribute requirementsin order of priority, where, as discussed above, each received attributerequirement is assigned a rating level. For example, for the attributerequirements of low cost, high CPU performance and high memory capacity,the user may assign having low cost with the highest priority, followedby high memory capacity and high CPU performance to form a list ofattribute requirements of the application workload in order of priorityas follows: low cost, high memory capacity and high CPU performance.

In step 609, administrative server 303 applies dependency graph 700 tothe list of attribute requirements of the application workload in orderof priority to determine if the attribute requirements of theapplication workload based on priority are supported by a hypervisorplatform 409 of the pools of compute nodes 302. For instance, referringto the example discussed in step 608, administrative server 303 appliesdependency graph 700 to the list (list of attribute requirements inorder of priority) of low cost, high memory capacity and high CPUperformance to determine if such a combination of attribute requirementsof the application workload based on priority are supported by ahypervisor platform 409 of the pools of compute nodes 302. Asillustrated in FIG. 7, when the attribute requirement of low cost hasthe highest priority, the combination of high memory capacity (secondhighest priority) and high CPU performance (third highest priority) isinfeasible. That is, there are no hypervisor platforms 409 that supportthe attribute requirements of the application workload based onpriority.

In step 610, a determination is made by administrative server 303 as towhether the attribute requirements of the application workload based onpriority are supported by a hypervisor platform 409 of the pools ofcompute nodes 302.

If the attribute requirements of the application workload based onpriority are not supported by a hypervisor platform 409 of the pools ofcompute nodes 302, then, in step 611, administrative server 303 informsthe user that the attribute requirements of application workload basedon priority are not supported by a hypervisor platform 409 and toprovide different levels of priority and/or rating levels for theworkload attributes. Administrative server 303 then receives differentlevels of priority and/or rating levels for the workload attributes fromthe user (e.g., user of client device 101) in step 608.

If, however, the attribute requirements of the application workloadbased on priority are supported by a hypervisor platform 409 of thepools of compute nodes 302, then, in step 612, administrative server 303applies a ranking algorithm to a list of pools of compute nodes 302 bycomparing the list of attribute requirements of the application workloadin order of priority with the hypervisor characteristics of the pools ofcompute nodes 302 to identify an ordered list of pools of compute nodes302 that are best suited for satisfying the attribute requirements ofthe application workload based on the received level of priorities. Forexample, the ranking algorithm may be based on minimizing the number ofdisagreements while assigning a higher weight to those attributerequirements with a higher priority. Hence, those pools of compute nodes302 with a hypervisor platform 409 with the fewest number ofdisagreements while satisfying those attribute requirements with thehighest priorities will be identified.

In another example, the ranking algorithm may be based on maximizing thenumber of agreements while assigning a higher weight to those attributerequirements with a higher priority. Hence, those pools of compute nodes302 with a hypervisor platform 409 with the greatest number ofagreements while satisfying those attribute requirements with thehighest priorities will be identified.

In a further example, the ranking algorithm may simply be based onselecting those pools of compute nodes 302 with a hypervisor platform409 that satisfies the attribute requirement with the highest priorityregardless of the other attribute requirements.

In step 613, administrative server 303 displays, such as via a displayof a user computing device (e.g., client device 101), the ordered listof the pools of compute nodes 302 that are best suited for satisfyingthe attribute requirements of the application workload based on thereceived level of priorities.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1-7. (canceled)
 8. A computer program product for selecting hypervisorplatforms that are best suited to process application workloads, thecomputer program product comprising a computer readable storage mediumhaving program code embodied therewith, the program code comprising theprogramming instructions for: receiving attribute requirements for anapplication workload; applying a ranking algorithm to a list of pools ofcompute nodes to identify an ordered list of pools of compute nodes thatare best suited for satisfying said attribute requirements of saidapplication workload by comparing hypervisor characteristics of saidpools of compute nodes with said attribute requirements of saidapplication workload, wherein each of said pools of compute nodescomprises a set of compute nodes that run on a particular hypervisorplatform, wherein said particular hypervisor platform has a uniquecombination of characteristics that correspond to a combination of a setof attribute requirements; and displaying said ordered list of pools ofcompute nodes that are best suited for satisfying said attributerequirements of said application workload.
 9. The computer programproduct as recited in claim 8, wherein the program code furthercomprises the programming instructions for: constructing a tuple vectorfor said application workload using said attribute requirements of saidapplication workload; and constructing a vector for each particularhypervisor platform of said pools of compute nodes using said uniquecombination of characteristics of said particular hypervisor platform.10. The computer program product as recited in claim 9, wherein theprogram code further comprises the programming instructions for:comparing said tuple vector of said application workload with saidvector for each particular hypervisor platform of said pools of computenodes; and applying said ranking algorithm to said list of pools ofcompute nodes using said comparison.
 11. The computer program product asrecited in claim 8, wherein the program code further comprises theprogramming instructions for: generating a dependency graph illustratingfeasible combinations of workload attribute requirements for differentlevels of attribute priority that are supported by hypervisor platformsof said pools of compute nodes.
 12. The computer program product asrecited in claim 11, wherein the program code further comprises theprogramming instructions for: receiving a level of priority for eachreceived attribute requirement of said application workload to form alist of attribute requirements of said application workload in order ofpriority; applying said dependency graph to said list of attributerequirements of said application workload in order of priority todetermine if said attribute requirements of said application workloadbased on priority are supported by a hypervisor platform of said poolsof compute nodes.
 13. The computer program product as recited in claim8, wherein the program code further comprises the programminginstructions for: receiving a level of priority for each receivedattribute requirement of said application workload to form a list ofattribute requirements of said application workload in order ofpriority; applying said ranking algorithm to said list of pools ofcompute nodes by comparing said list of attribute requirements of saidapplication workload in order of priority with said hypervisorcharacteristics of said pools of compute nodes to identify said orderedlist of pools of compute nodes that are best suited for satisfying saidattribute requirements of said application workload based on saidreceived level of priorities; and displaying said ordered list of poolsof compute nodes that are best suited for satisfying said attributerequirements of said application workload based on said received levelof priorities.
 14. The computer program product as recited in claim 8,wherein attributes of said application workload comprise one or more ofthe following: Central Processing Unit (CPU) capacity, memory capacity,disk capacity, network capacity, CPU performance, memory performance,disk performance, network performance, power consumption, cost ofservice, reliability requirements and service level agreement policies.15. A system, comprising: a memory unit for storing a computer programfor selecting hypervisor platforms that are best suited to processapplication workloads; and a processor coupled to the memory unit,wherein the processor is configured to execute the program instructionsof the computer program comprising: receiving attribute requirements foran application workload; applying a ranking algorithm to a list of poolsof compute nodes to identify an ordered list of pools of compute nodesthat are best suited for satisfying said attribute requirements of saidapplication workload by comparing hypervisor characteristics of saidpools of compute nodes with said attribute requirements of saidapplication workload, wherein each of said pools of compute nodescomprises a set of compute nodes that run on a particular hypervisorplatform, wherein said particular hypervisor platform has a uniquecombination of characteristics that correspond to a combination of a setof attribute requirements; and displaying said ordered list of pools ofcompute nodes that are best suited for satisfying said attributerequirements of said application workload.
 16. The system as recited inclaim 15, wherein the program instructions of the computer programfurther comprises: constructing a tuple vector for said applicationworkload using said attribute requirements of said application workload;and constructing a vector for each particular hypervisor platform ofsaid pools of compute nodes using said unique combination ofcharacteristics of said particular hypervisor platform.
 17. The systemas recited in claim 16, wherein the program instructions of the computerprogram further comprises: comparing said tuple vector of saidapplication workload with said vector for each particular hypervisorplatform of said pools of compute nodes; and applying said rankingalgorithm to said list of pools of compute nodes using said comparison.18. The system as recited in claim 15, wherein the program instructionsof the computer program further comprises: generating a dependency graphillustrating feasible combinations of workload attribute requirementsfor different levels of attribute priority that are supported byhypervisor platforms of said pools of compute nodes.
 19. The system asrecited in claim 18, wherein the program instructions of the computerprogram further comprises: receiving a level of priority for eachreceived attribute requirement of said application workload to form alist of attribute requirements of said application workload in order ofpriority; applying said dependency graph to said list of attributerequirements of said application workload in order of priority todetermine if said attribute requirements of said application workloadbased on priority are supported by a hypervisor platform of said poolsof compute nodes.
 20. The system as recited in claim 15, wherein theprogram instructions of the computer program further comprises:receiving a level of priority for each received attribute requirement ofsaid application workload to form a list of attribute requirements ofsaid application workload in order of priority; applying said rankingalgorithm to said list of pools of compute nodes by comparing said listof attribute requirements of said application workload in order ofpriority with said hypervisor characteristics of said pools of computenodes to identify said ordered list of pools of compute nodes that arebest suited for satisfying said attribute requirements of saidapplication workload based on said received level of priorities; anddisplaying said ordered list of pools of compute nodes that are bestsuited for satisfying said attribute requirements of said applicationworkload based on said received level of priorities.
 21. The system asrecited in claim 15, wherein attributes of said application workloadcomprise one or more of the following: Central Processing Unit (CPU)capacity, memory capacity, disk capacity, network capacity, CPUperformance, memory performance, disk performance, network performance,power consumption, cost of service, reliability requirements and servicelevel agreement policies.